Method for enhanced position estimation

Abstract

A method for enhanced position estimation using a distributed antenna system. The method includes: receiving input data based on at least one measurement resulting from a ranging procedure using a distributed antenna system, wherein the ranging procedure specifies measures for determining a distance dx depending on a signal propagation regarding the ranging procedure; providing at least one neural network model, wherein the at least one neural network model specifies a spatial pattern recognition and/or a temporal sequence to process the input data; combining the input data using the at least one neural network model to provide an enhanced position estimate; and providing a position result based on the combining.

What is claimed is: 
     
         1 . A method for enhanced position estimation using a distributed antenna system, the method comprising the following steps:
 receiving input data based on at least one measurement resulting from a ranging procedure using a distributed antenna system, wherein the ranging procedure specifies measures for determining a distance depending on a signal propagation regarding the ranging procedure;   providing at least one neural network model, wherein the at least one neural network model specifies a spatial pattern recognition and/or a temporal sequence to process the input data;   combining the input data using the at least one neural network model to provide an enhanced position estimate; and   providing a position estimate based on the combining.   
     
     
         2 . The method of  claim 1 , wherein the at least one neural network model includes a hybrid neural network model using a convolutional neural network and a multilayer perceptron to process the input data, and wherein, during the combining, the following further steps are performed:
 extracting spatial features from the received input data based on the convolutional neural network model; and   refining the spatial features to determine the position estimate depending on the multilayer perceptron.   
     
     
         3 . The method of  claim 1 , wherein the at least one neural network model includes a recurrent neural network model specifying the temporal sequence, and wherein, during the combining, the following further step is performed:
 processing sequential measurements regarding the ranging procedure based on the recurrent neural network model.   
     
     
         4 . The method of  claim 1 , wherein the at least one neural network model includes a fusion neural network model using a convolutional and a recurrent neural network model, and wherein the method further comprises the following steps:
 performing at least one preprocessing step to process the received input data;   assessing accuracy and/or robustness of the processed input data using metrics such as Root Mean Square Error and/or Mean Absolute Error; and   fusing the processed input data to improve a reliability of the position estimate.   
     
     
         5 . The method of  claim 1 , wherein the input data includes a channel impulse response, and/or a Received Signal Strength Indicator and/or ranging data based on the ranging procedure. 
     
     
         6 . The method of  claim 1 , further comprising at least one of the following steps:
 measuring a time of flight, and/or a received signal strength indicator, and/or a channel impulse response data based on the ranging procedure using the distributed antenna system;   calculating at least one range from a time of flight based on the ranging procedure and based on the distributed antenna system;   determining the position estimate based on the provided neural network model.   
     
     
         7 . The method of  claim 1 , wherein, during the combining, at least one of the following further steps is performed:
 performing a weighting average method of the combined input data;   performing a weighting average method of the combined input data, wherein weights of the weighting average method are dependent on external conditions including urban and/or rural scenarios.   
     
     
         8 . The method of  claim 1 , wherein the input data is based on ultra-wideband measurements based on a ranging procedure, between a vulnerable road user and a vehicle, wherein the vehicle includes an ultra-wideband antenna system includes at least two antennas. 
     
     
         9 . A training method for a neural network model, comprising the following steps:
 providing a dataset including multiple line of sight and/or non-line-of-sight scenarios when performing a ranging procedure;   initiating a ranging procedure between at least two objects, wherein at least one of the at least two objects includes a distributed antenna system;   receiving input data based on measurements regarding the ranging procedure, wherein the input data include ranging data and/or a channel impulse response and/or a Received Signal strength Indicator;   learning temporal and/or spatial correlations from the input data based on the measurements regarding the ranging procedure; and   adjusting weights of the neural network model through backpropagation and/or gradient descent algorithms.   
     
     
         10 . An apparatus, comprising:
 a data processing apparatus for enhanced position estimation using a distributed antenna system, the data processing apparatus configured to:
 receive input data based on at least one measurement resulting from a ranging procedure using a distributed antenna system, wherein the ranging procedure specifies measures for determining a distance depending on a signal propagation regarding the ranging procedure, 
 provide at least one neural network model, wherein the at least one neural network model specifies a spatial pattern recognition and/or a temporal sequence to process the input data; 
 combine the input data using the at least one neural network model to provide an enhanced position estimate, and 
 provide a position estimate based on the combining. 
   
     
     
         11 . A non-transitory computer-readable storage medium on which are stored instructions for enhanced position estimation using a distributed antenna system, the instructions, when executed by a computer, causing the computer to perform the following steps:
 receiving input data based on at least one measurement resulting from a ranging procedure using a distributed antenna system, wherein the ranging procedure specifies measures for determining a distance depending on a signal propagation regarding the ranging procedure;   providing at least one neural network model, wherein the at least one neural network model specifies a spatial pattern recognition and/or a temporal sequence to process the input data;   combining the input data using the at least one neural network model to provide an enhanced position estimate; and   providing a position estimate based on the combining.